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Intelligent control of energy generation, storage, and usage is indispensable for use in a standalone supply system powered by local and renewable energy resources intended to provide uninterrupted power for mission-critical infrastructures. The probabilistic nature of wind and solar energy sources requires weather forecast information to manage and prioritize the available storage level, critical loads, non-critical loads that need to be fed on a certain time schedule, and deferrable loads. This is especially important since these local power generation systems will need to be able to provide power during seasonal and daily adjustments of anticipated renewable resource availability. For example, not only may there be seasonal adjustments to the amount of sunlight available to a specific location, there may also be daily fluctuations in the clarity of the atmosphere due to fog, smog and other variables that can impact the availability of solar energy on a particular day.
The Sustainable Energy Load Flow Management System (SelfMaster™) is outlined in FIG. 1. SelfMaster™ is a microcomputer-based controller. User input defines a schedule of activities and priority of energy usage. SelfMaster™ receives forecast data from a weather service and input data from sensors monitoring generated power, storage levels, and electric demand in real time. The computer software continuously updates a database of actual component input-output characteristics. The near-future performance of the system is simulated using estimated states of the generated and stored energy, scheduled activities, and forecast data within a reasonable time interval. The storage level is assessed and optimal load distribution is determined for critical, deferrable, and non-critical loads. Computer interface circuits turn the loads on or off and provide output for variable frequency drives (VFD) that control heating, ventilation, and air conditioning (HVAC) devices.